• Journal of the Korean Society of Marine Environment & Safety
• /
• v.24 no.5
• /
• pp.504-510
• /
• 2018

Marine casualties are caused mainly by collisions and grounding, due to human error. The SMART Navigation Service is preparing a measure to reduce marine casualties caused by human error and establish an LTE Accident Vulnerability Monitoring System (SV10) to evaluate the danger of collision or grounding for a vessel based on location information collected on land. This service will also share real-time vessel locations and danger information with related agencies to enable them to respond more quickly to accidents on land. In this study, statistical reports on marine casualties and investigation reports provided by the Korea Maritime Safety Tribunal are analyzed, so the percentage of marine casualties that could be reduced using the SV10 service could be identified.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.24 no.4
• /
• pp.38-47
• /
• 2020

A parametric study for analytical correction using measurement results was performed to minimize errors in the predictions of column shortening in RC tall building. The parameters of the column shortening analytical correction are the execution standard of analytical correction, the value of the analytical correction, and the measurement location, and the analytical correction models with the parameters were applied to the construction sequence analysis of a 41-story RC building to compare and analyze the correction effect according to the parameter. The reduction ratio of the error value for each floor was compared with the number of corrections and the total corrected value, and it was confirmed that the error tended to be minimized when the execution standard of analytical correction was performed based on a regular interval, when the analysis correction value was corrected by the error value, and when the measurement position was measured every floor. From this, it was confirmed that the most appropriate analytical correction model can be derived by applying multiple analytical correction models to the actual analysis model.

• Journal of the Ergonomics Society of Korea
• /
• v.30 no.1
• /
• pp.75-86
• /
• 2011

This paper describes several current research activities and the field techniques for the prevention of human errors during the operation of nuclear power plants(NPPs). The human aspects such as 'fitness for the duties', 'job competence and suitability', 'types of communication', 'behaviors of field workers', 'teamwork of main control room crews', 'task procedures', etc. have been investigated for improving the performance of operating personnel in NPPs. We decide to develop a set of the complementary techniques for the reduction of human errors. The set of techniques developed includes teamwork criteria, jobs fitness analysis, procedure enhancement guide, 3-way communication, campaign posters, a behavior based safety program, a procedure guideline, and a task hazard identification method for the field practitioners in NPPs. These can offer a set of significant human error countermeasures to be considered for analyzing and reducing human error in NPPs as well as other fields of industry.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.44 no.6 s.360
• /
• pp.1-8
• /
• 2007

In wireless sensor network which uses limited battery, power consumption is very important factor for the survivality of the system. By using low-power communication to reduce power consumption, error rate is increased in typical conditions. This paper analyzes power consumption of specific error control coding (ECC) implementations. With identical link quality, ECC provides coding gain which save the power for transmission at the cost of computing power. In sensor node, transmit power is higher than computing power of Micro Controller Unit (MCU). In this paper, Viterbi algerian is applied to the low-transmit-power sensor networks in terms of network power consumption. Practically, Viterbi algorithm presents 20% of reduction of re-transmission in compared with Auto Repeat Request (ARQ) system. Furthermore, it is observed that network power consumption is decreased by almost 18%.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.5C
• /
• pp.479-486
• /
• 2010

In this paper, we propose a novel low-complexity Alamouti coded orthogonal frequency division multiplexing (OFDM) scheme for asynchronous cooperative communications. Exploiting the combination of OFDM symbols at the source node and simple operations including sign change and complex product at the relay node, the proposed scheme can achieve cooperative diversity gain without use of time-reversion and shifting operations that the conventional scheme proposed by Li and Xia needs. In addition, by using the cyclic prefix (CP) removal and insertion operations at the relay node, the proposed scheme does not suffer from a considerable degradation of bit-error-rate (BER) performance even though perfect timing synchronization is not achieved at the relay node. From the simulation results, it is demonstrated that the BER performance of the proposed scheme is much superior to that of the conventional scheme in the presence of timing synchronization error at the relay node. It is also shown that the proposed scheme obtains two times higher diversity gain compared with the conventional scheme at the cost of half reduction in transmission efficiency.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.11
• /
• pp.31-38
• /
• 2013

Feature normalization as a pre-processing step has been widely used in classification problems to reduce the effect of different scale in each feature dimension and error as a result. Most of the existing methods, however, assume some distribution function on feature distribution. Even worse, existing methods do not use the labels of data points and, as a result, do not guarantee the optimality of the normalization results in classification. In this paper, proposed is a supervised rank normalization which combines rank normalization and a supervised learning technique. The proposed method does not assume any feature distribution like rank normalization and uses class labels of nearest neighbors in classification to reduce error. SVM, in particular, tries to draw a decision boundary in the middle of class overlapping zone, the reduction of data density in that area helps SVM to find a decision boundary reducing generalized error. All the things mentioned above can be verified through experimental results.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.6
• /
• pp.987-994
• /
• 2020

In this paper, we studied the effect of kernel size of CNN layer on performance in acoustic noise attenuators. This system uses a deep learning algorithm using a neural network adaptive prediction filter instead of using the existing adaptive filter. Speech is estimated from a single input speech signal containing noise using a 100-neuron, 16-filter CNN filter and an error back propagation algorithm. This is to use the quasi-periodic property in the voiced sound section of the voice signal. In this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed to verify the performance of the noise attenuator for the kernel size. As a result of the simulation, when the kernel size is about 16, the MSE and MAE values are the smallest, and when the size is smaller or larger than 16, the MSE and MAE values increase. It can be seen that in the case of an speech signal, the features can be best captured when the kernel size is about 16.

• Journal of the Institute of Convergence Signal Processing
• /
• v.21 no.2
• /
• pp.55-60
• /
• 2020

In this paper, we propose a fast block matching algorithm of motion estimation using early detection of optimal candidate with high priority and a threshold. Even though so many fast algorithms for motion estimation have been published to reduce computational reduction full search algorithm, still so many works to improve performance of motion estimation are being reported. The proposed algorithm calculates block matching error for each candidate with high priority from previous partial matching error. The proposed algorithm can be applied additionally to most of conventional fast block matching algorithms for more speed up. By doing that, we can find the minimum error point early and get speed up by reducing unnecessary computations of impossible candidates. The proposed algorithm uses smaller computation than conventional fast full search algorithms with the same prediction quality as the full search algorithm. Experimental results shows that the proposed algorithm reduces 30~70% compared with the computation of the PDE and full search algorithms without any degradation of prediction quality and further reduces it with other fast lossy algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.9
• /
• pp.1180-1186
• /
• 2020

One of the Embedded systems Tiny Processing Unit (TPU) usually acts in harsh environments like external shock or insufficient power. In these cases, data could be polluted, and cause critical problems. As a solution to data pollution, many embedded systems are using Error Correcting Code (ECC) to protect and restore data. However, ECC processing in TPU increases the overall processing time by increasing the time of instruction fetch which is the bottleneck. In this paper, we propose an architecture of parallelized ECC block to the reduce bottleneck of TPU. The proposed architecture results in the reduction of time 10% compared to the original model, although memory usage increased slightly. The test is evaluated with a matrix product that has various instructions. TPU with proposed parallelized ECC block shows 7% faster than the original TPU with ECC and was able to perform the proposed test accurately.

• The Journal of the Acoustical Society of Korea
• /
• v.28 no.8
• /
• pp.815-821
• /
• 2009

Information processing in variable and noisy environments is usually accomplished by means of adaptive filters. Among various adaptive algorithms, Least Mean Square (LMS) has become the most popular for its robustness, good tracking capabilities and simplicity, both in terms of computational load and easiness of implementation. In practical application of the LMS algorithm, the most important key parameter is the Step Size. As is well known, if the Step Size is large, the convergence rate of the algorithm will be rapid, but the steady state mean square error (MSE) will increase. On the other hand, if the Step Size is small, the steady state MSE will be small, but the convergence rate will be slow. Many researches have been proposed to alleviate this drawback by using a variable Step Size. In this paper, a new variable Step Size LMS(VSSLMS) called Categorized VSSLMS (CVSSLMS) is proposed. CVSSLMS updates the Step Size by categorizing the current status of the gradient, hence significantly improves the convergence rate. The performance of the proposed algorithm was verified from the view point of convergence rate, Excessive Mean Square Error(EMSE), and complexity through experiments.